adapter: lora
base_model: microsoft/Phi-3.5-mini-instruct
bf16: true
chat_template: llama3
data_processes: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 0cbad7e17fb5b410_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0cbad7e17fb5b410_train_data.json
  type:
    field_instruction: prompt
    field_output: chosen
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: filipesantoscv11/54f8a375-a818-401f-ad52-173bba2e25a6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1.01e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.03
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/G.O.D/0cbad7e17fb5b410_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: cb1fc2ef-afd6-4b5f-a57d-24d433294f8d
wandb_project: cold9
wandb_run: your_name
wandb_runid: cb1fc2ef-afd6-4b5f-a57d-24d433294f8d
warmup_steps: 20
weight_decay: 0.0
xformers_attention: null

54f8a375-a818-401f-ad52-173bba2e25a6

This model is a fine-tuned version of microsoft/Phi-3.5-mini-instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2348

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1.01e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20
training_steps: 200

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0000	1	2.2327
1.6393	0.0021	50	1.4103
1.1938	0.0041	100	1.2686
1.2439	0.0062	150	1.2410
1.268	0.0082	200	1.2348

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1